SoroushVahidi/scidocs huggingface dataset:
FEEL: Featured Event Embedding Learning
(2018)

Statistical script learning is an effective way to acquire world knowledge which can be used for commonsense reasoning. Statistical script learning induces this knowledge by observing event sequences generated from texts. The learned model thus can predict subsequent events, given earlier events. Recent approaches rely on learning event embeddings which capture script knowledge. In this work, we suggest a general learning model–Featured Event Embedding Learning (FEEL)–for injecting event embeddings with fine grained information. In addition to capturing the dependencies between subsequent events, our model can take into account higher level abstractions of the input event which help the model generalize better and account for the global context in which the event appears. We evaluated our model over three narrative cloze tasks, and showed that our model is competitive with the most recent state-of-the-art. We also show that our resulting embedding can be used as a strong representation for advanced semantic tasks such as discourse parsing and sentence semantic relatedness.
Attribute-based encryption with non-monotonic access structures
(2007)

We construct an Attribute-Based Encryption (ABE) scheme that allows a user's private key to be expressed in terms of any access formula over attributes. Previous ABE schemes were limited to expressing only monotonic access structures. We provide a proof of security for our scheme based on the Decisional Bilinear Diffie-Hellman (BDH) assumption. Furthermore, the performance of our new scheme compares favorably with existing, less-expressive schemes.
Browser Fingerprinting as user tracking technology
(2017)

The Web has become an indispensable part of our society and is currently most commonly used mode of information delivery. Millions of users access the free services provided by the websites on daily basis and while providing these free services websites track and profile their web users. In this environment, the ability to track users and their online habits can be very lucrative for advertising companies, yet very intrusive for the privacy of users. The objective of this paper is to study about the increasingly common yet hardly discussed technique of identifying individual Web users and tracking them across multiple websites known as “Browser Fingerprinting”. A unique browser fingerprint is derived by the unique pattern of information visible whenever a computer visits a website. The permutations thus collected are sufficiently distinct that they can be used as a tool for tracking. Unlike cookies, Fingerprints are generated on server side and are difficult for a user to influence. The main objective of this research is study about how the fingerprinting was evolved, its positives and negatives, what threat it poses to users' online privacy and what countermeasures could be used to prevent it. This paper will also analyse which different properties the browsers send to the server, allowing a unique fingerprint of those browsers to be created.
Zone Divisional Hierarchical Routing Protocol for Wireless Sensor Network
(2016)

Clustering prolongs energy resources, improves scalability and preserves communication bandwidth. Clustering is either classified as static and dynamic or as equal and unequal. In cluster based routing protocols that employ multi-hop communication, imbalanced energy consumption among the nodes results in hot-spots. Unequal clustering overcomes hot-spots but requires a high overhead and is prone to connectivity issues. To offer guaranteed connectivity and alleviate the hot-spot problem, a zone divisional hierarchical routing protocol has been proposed in this paper. The network is divided into equal sized static rectangular clusters which are assigned to two zones namely near zone and far zone. The zone facing the base station is known as the near zone and rest of the network space makes up the far zone which is further divided into sub-zones. Dual cluster heads approach for sharing the reception, aggregation and forwarding tasks is proposed. The performance evaluation of the proposed protocol against the existing protocols reveals that the method offers energy-efficient multi-hop communication support, uses negligible overhead, prevents creation of hot-spots, avoids early death of nodes, uses balanced energy consumption across the network and maximizes the network lifetime.
MOBA: a New Arena for Game AI
(2017)

Games have always been popular testbeds for Artificial Intelligence (AI). In the last decade, we have seen the rise of the Multiple Online Battle Arena (MOBA) games, which are the most played games nowadays. In spite of this, there are few works that explore MOBA as a testbed for AI Research. In this paper we present and discuss the main features and opportunities offered by MOBA games to Game AI Research. We describe the various challenges faced along the game and also propose a discrete model that can be used to better understand and explore the game. With this, we aim to encourage the use of MOBA as a novel research platform for Game AI.
A 20-GHz phase-locked loop for 40-gb/s serializing transmitter in 0.13-/spl mu/m CMOS
(2006)

A 20-GHz phase-locked loop with 4.9 ps/sub pp//0.65 ps/sub rms/ jitter and -113.5 dBc/Hz phase noise at 10-MHz offset is presented. A half-duty sampled-feedforward loop filter that simply replaces the resistor with a switch and an inverter suppresses the reference spur down to -44.0 dBc. A design iteration procedure is outlined that minimizes the phase noise of a negative-g/sub m/ oscillator with a coupled microstrip resonator. Static frequency dividers made of pulsed latches operate faster than those made of flip-flops and achieve near 2:1 frequency range. The phase-locked loop fabricated in a 0.13-/spl mu/m CMOS operates from 17.6 to 19.4GHz and dissipates 480mW.
Color Balance and Fusion for Underwater Image Enhancement
(2018)

We introduce an effective technique to enhance the images captured underwater and degraded due to the medium scattering and absorption. Our method is a single image approach that does not require specialized hardware or knowledge about the underwater conditions or scene structure. It builds on the blending of two images that are directly derived from a color-compensated and white-balanced version of the original degraded image. The two images to fusion, as well as their associated weight maps, are defined to promote the transfer of edges and color contrast to the output image. To avoid that the sharp weight map transitions create artifacts in the low frequency components of the reconstructed image, we also adapt a multiscale fusion strategy. Our extensive qualitative and quantitative evaluation reveals that our enhanced images and videos are characterized by better exposedness of the dark regions, improved global contrast, and edges sharpness. Our validation also proves that our algorithm is reasonably independent of the camera settings, and improves the accuracy of several image processing applications, such as image segmentation and keypoint matching.
Rationale-Augmented Convolutional Neural Networks for Text Classification
(2016)

We present a new Convolutional Neural Network (CNN) model for text classification that jointly exploits labels on documents and their constituent sentences. Specifically, we consider scenarios in which annotators explicitly mark sentences (or snippets) that support their overall document categorization, i.e., they provide rationales. Our model exploits such supervision via a hierarchical approach in which each document is represented by a linear combination of the vector representations of its component sentences. We propose a sentence-level convolutional model that estimates the probability that a given sentence is a rationale, and we then scale the contribution of each sentence to the aggregate document representation in proportion to these estimates. Experiments on five classification datasets that have document labels and associated rationales demonstrate that our approach consistently outperforms strong baselines. Moreover, our model naturally provides explanations for its predictions.
Informing Pedagogical Action: Aligning Learning Analytics With Learning Design
(2013)

This article considers the developing field of learning analytics and argues that to move from small-scale practice to broad scale applicability, there is a need to establish a contextual framework that helps teachers interpret the information that analytics provides. The article presents learning design as a form of documentation of pedagogical intent that can provide the context for making sense of diverse sets of analytic data. We investigate one example of learning design to explore how broad categories of analytics—which we call checkpoint and process analytics—can inform the interpretation of outcomes from a learning design and facilitate pedagogical action.
Maternal depression and early childhood growth in developing countries: systematic review and meta-analysis.
(2011)

OBJECTIVE
To investigate the relationship between maternal depression and child growth in developing countries through a systematic literature review and meta-analysis.


METHODS
Six databases were searched for studies from developing countries on maternal depression and child growth published up until 2010. Standard meta-analytical methods were followed and pooled odds ratios (ORs) for underweight and stunting in the children of depressed mothers were calculated using random effects models for all studies and for subsets of studies that met strict criteria on study design, exposure to maternal depression and outcome variables. The population attributable risk (PAR) was estimated for selected studies.


FINDINGS
Seventeen studies including a total of 13,923 mother and child pairs from 11 countries met inclusion criteria. The children of mothers with depression or depressive symptoms were more likely to be underweight (OR: 1.5; 95% confidence interval, CI: 1.2-1.8) or stunted (OR: 1.4; 95% CI: 1.2-1.7). Subanalysis of three longitudinal studies showed a stronger effect: the OR for underweight was 2.2 (95% CI: 1.5-3.2) and for stunting, 2.0 (95% CI: 1.0-3.9). The PAR for selected studies indicated that if the infant population were entirely unexposed to maternal depressive symptoms 23% to 29% fewer children would be underweight or stunted.


CONCLUSION
Maternal depression was associated with early childhood underweight and stunting. Rigorous prospective studies are needed to identify mechanisms and causes. Early identification, treatment and prevention of maternal depression may help reduce child stunting and underweight in developing countries.
Application of Artificial Neural Networks to Multiple Criteria Inventory Classification
(2010)

Original scientific paper Inventory classification is a very important part of inventory control which represents the technique of operational research discipline. A systematic approach to the inventory control and classification may have a significant influence on company competitiveness. The paper describes the results obtained by investigating the application of neural networks in multiple criteria inventory classification. Various structures of a back-propagation neural network have been analysed and the optimal one with the minimum Root Mean Square error selected. The predicted results are compared to those obtained by the multiple criteria classification using the analytical hierarchy process.
Methods for Pretreatment of Lignocellulosic Biomass for Efficient Hydrolysis and Biofuel Production
(2009)

Industrial & Engineering Chemistry Research is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Review Methods for Pretreatment of Lignocellulosic Biomass for Efficient Hydrolysis and Biofuel Production Parveen Kumar, Diane M. Barrett, Michael J. Delwiche, and Pieter Stroeve Ind. Eng. Chem. Res., Article ASAP • DOI: 10.1021/ie801542g • Publication Date (Web): 20 March 2009 Downloaded from http://pubs.acs.org on March 26, 2009
Automatic Game Design via Mechanic Generation
(2014)

Game designs often center on the game mechanics— rules governing the logical evolution of the game. We seek to develop an intelligent system that generates computer games. As first steps towards this goal we present a composable and cross-domain representation for game mechanics that draws from AI planning action representations. We use a constraint solver to generate mechanics subject to design requirements on the form of those mechanics—what they do in the game. A planner takes a set of generated mechanics and tests whether those mechanics meet playability requirements—controlling how mechanics function in a game to affect player behavior. We demonstrate our system by modeling and generating mechanics in a role-playing game, platformer game, and combined role-playing-platformer game.
Central Retinal Artery Occlusion and Partial Ophthalmoplegia Caused by Hyaluronic Acid Injection Filler Cosmetic
(2018)

Central retinal artery occlusion is a rare ocular emergency condition that can lead to a total blindness. Hereby we report a 30-year-old woman with a sudden visual loss of the left eye after being injected by hyaluronic acid filler at the dorsum nasal area for cosmetic purpose. Visual acuity was light perception while anterior segment and intraocular pressure were within normal limit. Partial ophthalmoplegia include restriction to the nasal gaze were found. Funduscopic examination revealed total retinal edema with a cherry red spot at the fovea. Ocular massage was performed as initial management followed by humor aqueous paracentesis. All procedures were done within 90 minutes. The patients discharge with the visual improvement to hand motion and the Partial ophthalmophlegia has been improved after being evaluated less than a month.
Critiquing-based recommenders: survey and emerging trends
(2011)

Critiquing-based recommender systems elicit users’ feedback, called critiques, which they made on the recommended items. This conversational style of interaction is in contract to the standard model where users receive recommendations in a single interaction. Through the use of the critiquing feedback, the recommender systems are able to more accurately learn the users’ profiles, and therefore suggest better recommendations in the subsequent rounds. Critiquing-based recommenders have been widely studied in knowledge-, content-, and preference-based recommenders and are beginning to be tried in several online websites, such as MovieLens. This article examines the motivation and development of the subject area, and offers a detailed survey of the state of the art concerning the design of critiquing interfaces and development of algorithms for critiquing generation. With the help of categorization analysis, the survey reveals three principal branches of critiquing based recommender systems, using respectively natural language based, system-suggested, and user-initiated critiques. Representative example systems will be presented and analyzed for each branch, and their respective pros and cons will be discussed. Subsequently, a hybrid framework is developed to unify the advantages of different methods and overcome their respective limitations. Empirical findings from user studies are further presented, indicating how hybrid critiquing supports could effectively enable end-users to achieve more confident decisions. Finally, the article will point out several future trends to boost the advance of critiquing-based recommenders.
Expert Level Control of Ramp Metering Based on Multi-Task Deep Reinforcement Learning
(2018)

This paper shows how the recent breakthroughs in reinforcement learning (RL) that have enabled robots to learn to play arcade video games, walk, or assemble colored bricks, can be used to perform other tasks that are currently at the core of engineering cyberphysical systems. We present the first use of RL for the control of systems modeled by discretized non-linear partial differential equations (PDEs) and devise a novel algorithm to use non-parametric control techniques for large multi-agent systems. Cyberphysical systems (e.g., hydraulic channels, transportation systems, the energy grid, and electromagnetic systems) are commonly modeled by PDEs, which historically have been a reliable way to enable engineering applications in these domains. However, it is known that the control of these PDE models is notoriously difficult. We show how neural network-based RL enables the control of discretized PDEs whose parameters are unknown, random, and time-varying. We introduce an algorithm of mutual weight regularization (MWR), which alleviates the curse of dimensionality of multi-agent control schemes by sharing experience between agents while giving each agent the opportunity to specialize its action policy so as to tailor it to the local parameters of the part of the system it is located in. A discretized PDE, such as the scalar Lighthill–Whitham–Richards PDE can indeed be considered as a macroscopic freeway traffic simulator and which presents the most salient challenges for learning to control large cyberphysical system with multiple agents. We consider two different discretization procedures and show the opportunities offered by applying deep reinforcement for continuous control on both. Using a neural RL PDE controller on a traffic flow simulation based on a Godunov discretization of the San Francisco Bay Bridge, we are able to achieve precise adaptive metering without model calibration thereby beating the state of the art in traffic metering. Furthermore, with the more accurate BeATS simulator, we manage to achieve a control performance on par with ALINEA, a state-of-the-art parametric control scheme, and show how using MWR improves the learning procedure.
Design Issues and Challenges in Wireless Sensor Networks
(2015)

Wireless Sensor Networks (WSNs) are composed self-organized wireless ad hoc networks which comprise of a large number of resource constrained sensor nodes. The major areas of research in WSN is going on hardware, and operating system of WSN, deployment, architecture, localization, synchronization, programming models, data aggregation and dissemination, database querying, architecture, middleware, quality of service and security. This paper study highlights ongoing research activities and issues that affect the design and performance of Wireless Sensor Network.
A Persona-Based Neural Conversation Model
(2016)

We present persona-based models for handling the issue of speaker consistency in neural response generation. A speaker model encodes personas in distributed embeddings that capture individual characteristics such as background information and speaking style. A dyadic speakeraddressee model captures properties of interactions between two interlocutors. Our models yield qualitative performance improvements in both perplexity and BLEU scores over baseline sequence-to-sequence models, with similar gains in speaker consistency as measured by human judges.
Intelligent Tutoring Systems with Conversational Dialogue
(2001)

been developed during the last 20 years have proven to be quite successful, particularly in the domains of mathematics, science, and technology. They produce significant learning gains beyond classroom environments. They are capable of engaging most students’ attention and interest for hours. We have been working on a new generation of intelligent tutoring systems that hold mixedinitiative conversational dialogues with the learner. The tutoring systems present challenging problems and questions to the learner, the learner types in answers in English, and there is a lengthy multiturn dialogue as complete solutions or answers evolve. This article presents the tutoring systems that we have been developing. AUTOTUTOR is a conversational agent, with a talking head, that helps college students learn about computer literacy. ANDES, ATLAS, AND WHY2 help adults learn about physics. Instead of being mere information-delivery systems, our systems help students actively construct knowledge through conversations.
Attention with Intention for a Neural Network Conversation Model
(2015)

In a conversation or a dialogue process, attention and intention play intrinsic roles. This paper proposes a neural network based approach that models the attention and intention processes. It essentially consists of three recurrent networks. The encoder network is a word-level model representing source side sentences. The intention network is a recurrent network that models the dynamics of the intention process. The decoder network is a recurrent network that produces responses to the input from the source side. It is a language model that is dependent on the intention and has an attention mechanism to attend to particular source side words, when predicting a symbol in the response. The model is trained end-toend without labeling data. Experiments show that this model generates natural responses to user inputs.
Low cost power failure protection for MLC NAND flash storage systems with PRAM/DRAM hybrid buffer
(2013)

In the latest PRAM/DRAM hybrid MLC NAND flash storage systems (NFSS), DRAM is used to temporarily store file system data for system response time reduction. To ensure data integrity, super-capacitors are deployed to supply the backup power for moving the data from DRAM to NAND flash during power failures. However, the capacitance degradation of super-capacitor severely impairs system robustness. In this work, we proposed a low cost power failure protection scheme to reduce the energy consumption of power failure protection and increase the robustness of the NFSS with PRAM/DRAM hybrid buffer. Our scheme enables the adoption of the more reliable regular capacitor to replace the super capacitor as the backup power. The experimental result shows that our scheme can substantially reduce the capacitance budget of power failure protection circuitry by 75.1% with very marginal performance and energy overheads.
Latent Variable Analysis Growth Mixture Modeling and Related Techniques for Longitudinal Data
(2004)

This chapter gives an overview of recent advances in latent variable analysis. Emphasis is placed on the strength of modeling obtained by using a flexible combination of continuous and categorical latent variables. To focus the discussion and make it manageable in scope, analysis of longitudinal data using growth models will be considered. Continuous latent variables are common in growth modeling in the form of random effects that capture individual variation in development over time. The use of categorical latent variables in growth modeling is, in contrast, perhaps less familiar, and new techniques have recently emerged. The aim of this chapter is to show the usefulness of growth model extensions using categorical latent variables. The discussion also has implications for latent variable analysis of cross-sectional data. The chapter begins with two major parts corresponding to continuous outcomes versus categorical outcomes. Within each part, conventional modeling using continuous latent variables will be described